Lambda has announced a multibillion-euro agreement with Microsoft to expand AI infrastructure powered by tens of thousands of NVIDIA GPUs, marking one of the largest private cloud computing collaborations to date.
The multi-year deal aims to accelerate the deployment of AI supercomputers at scale, enhancing the capacity for enterprise and research applications across industries.
A collaboration that builds on an eight-year relationship between the two companies and reflects growing global demand for high-performance computing driven by the rise of AI assistants and enterprise AI solutions.
Stephen Balaban, CEO of Lambda, said the project represents a major step in developing gigawatt-scale AI factories capable of serving billions of users. The company positions itself as a trusted large-scale partner for organisations building advanced AI models and systems.
Founded in 2012, Lambda designs supercomputing infrastructure for AI training and inference, aiming to make computing power as accessible as electricity and to advance what it calls the era of ‘superintelligence’.
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AI is inserting itself between companies and customers, Cloudflare CEO Matthew Prince warned in Toronto. More people ask chatbots before visiting sites, dulling brands’ impact. Even research teams lose revenue as investors lean on AI summaries.
Frontier models devour data, pushing firms to chase exclusive sources. Cloudflare lets publishers block unpaid crawlers to reclaim control and compensation. The bigger question, said Prince, is which business model will rule an AI-mediated internet.
Policy scrutiny focuses on platforms that blend search with AI collection. Prince urged governments to separate Google’s search access from AI crawling to level the field. Countries that enforce a split could attract publishers and researchers seeking predictable rules and payment.
Licensing deals with news outlets, Reddit, and others coexist with scraping disputes and copyright suits. Google says it follows robots.txt, yet testimony indicated AI Overviews can use content blocked by robots.txt for training. Vague norms risk eroding incentives to create high-quality online content.
A practical near-term playbook combines technical and regulatory steps. Publishers should meter or block AI crawlers that do not pay. Policymakers should require transparency, consent, and compensation for high-value datasets, guiding the shift to an AI-mediated web that still rewards creators.
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Alibaba unveiled Qwen3-Max-Thinking, which scored 100 percent on AIME 2025 and HMMT, matching OpenAI’s top model on reasoning tests. It targets high-precision problem-solving across algebra, number theory, and probability. Researchers regard elite maths contests as strong proxies for reasoning.
Built on Qwen3-Max, a trillion-parameter flagship, the thinking variant emphasises step-by-step solutions. Alibaba says it matches or beats Claude Opus 4, DeepSeek V3.1, Grok 4, and GPT-5 Pro. Positioning stresses accuracy, traceability, and controllable latency.
Signal from a live trading trial added momentum. In a two-week crypto experiment, Qwen3-Max returned 22.3 percent on 10,000 US dollars. Competing systems underperformed, with DeepSeek at 4.9 percent and several US models booking losses.
Access is available via the Qwen web chatbot and Alibaba Cloud APIs. Early adopters can test tool use and stepwise reasoning on technical tasks. Enterprises are exploring finance, research, and operations cases requiring reliability and auditability.
Alibaba researchers say further tuning will broaden task coverage without diluting peak maths performance. Plans include multilingual reasoning, safety alignment, and robustness under distribution shift. Community benchmarks and contests will track progress.
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The European Commission has approved €2.9 billion in funding for 61 large-scale net-zero technology projects, marking one of the EU’s most significant investments in clean innovation to date.
Financed through revenues from the EU Emissions Trading System, the initiative aims to accelerate Europe’s path towards climate neutrality by 2050.
The selected projects cover 19 industrial sectors across 18 Member States and target areas such as renewable energy, energy storage, zero-emission mobility, and industrial carbon management.
Collectively, they are expected to cut more than 220 million tonnes of CO₂ over the next decade, reinforcing Europe’s global leadership in sustainable technologies instead of relying on imports.
Funded under the Innovation Fund, which draws on an estimated €40 billion in ETS revenues, the initiative highlights the EU’s industrial readiness for decarbonisation. The latest call attracted 359 applications requesting €21.7 billion in support, underscoring the rapid growth of the continent’s cleantech sector.
Commissioner Wopke Hoekstra described the announcement as proof that the EU is turning its climate ambitions into industrial reality, creating green jobs and strengthening economic resilience. The next round of Innovation Fund calls will open in December 2025.
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The European Commission has unveiled RAISE, a new virtual institute designed to unite Europe’s AI research and accelerate scientific breakthroughs.
The launch, announced in Copenhagen, marks a flagship moment in the EU’s strategy to strengthen its leadership in science and technology through collective action.
Funded with €107 million under Horizon Europe, RAISE will bring together Europe’s best resources in data, computing power, and research talent.
An initiative that will help scientists apply AI to pressing challenges such as cancer treatment, climate change, and natural disaster prediction, while promoting innovation that serves humanity instead of commercial interests alone.
RAISE will work with the EuroHPC Joint Undertaking to secure access to AI Gigafactories and will dedicate €75 million to train and attract global researchers through Networks of Excellence.
The Commission also plans to double Horizon Europe’s annual AI investments to more than €3 billion, ensuring that the EU remains a global leader in scientific AI.
A project that reflects the EU’s ambition to achieve technological sovereignty and create an inclusive AI ecosystem. As RAISE grows in phases towards 2034, it will strengthen cooperation among Member States, academia, and industry, setting a benchmark for responsible and innovative AI in science.
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The European Commission has approved the creation of the Digital Commons European Digital Infrastructure Consortium (DC-EDIC), designed to strengthen Europe’s digital sovereignty. The new body unites France, Germany, the Netherlands and Italy as founding members.
DC-EDIC aims to build open, interoperable and sovereign digital systems, reducing reliance on imported technologies. Its work will focus on shared data infrastructure, connected public administration and collaborative digital tools to support both governments and businesses.
The Paris-based consortium will coordinate funding access, offer legal and technical guidance, and support the scaling of open-source digital solutions across Europe. Future projects will include a one-stop shop for resources, an expertise hub and a Digital Commons Forum.
All jointly developed software will be released under free, open-source licences, ensuring transparency and reuse whilst being GDPR compliant. The official launch is expected in December 2025, with the first annual State of the Digital Commons report planned for 2027.
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Organisations across public and private sectors are using Salesforce’s Agentforce to engage people whenever and wherever they need support.
From local governments to hospitals and education platforms, AI systems are transforming how services are delivered and accessed.
In the city of Kyle, Texas, an Agentforce-driven 311 app enables residents to report issues such as potholes or water leaks. The city plans to make the system voice-enabled, reducing traditional call volumes while maintaining a steady flow of service requests and faster responses.
At Pearson, AI enables students to access their online learning platforms instantly, regardless of their time zone. The company stated that the technology fosters loyalty by providing immediate assistance, rather than requiring users to wait for human support.
Meanwhile, UChicago Medicine utilises AI to streamline patient interactions, from prescription refills to scheduling, while ambient listening tools enable doctors to focus entirely on patients rather than typing notes.
Salesforce said Agentforce empowers organisations to save resources while enhancing trust, accessibility, and service quality. By meeting people on their own terms, AI enables more responsive and human-centred interactions across various industries.
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Amazon Web Services (AWS) and OpenAI have entered a $38 billion, multi-year partnership that will see OpenAI run and scale its AI workloads on AWS infrastructure. The seven-year deal grants OpenAI access to vast NVIDIA GPU clusters and the capacity to scale to millions of CPUs.
The collaboration aims to meet the growing global demand for computing power driven by rapid advances in generative AI.
OpenAI will immediately begin using AWS compute resources, with all capacity expected to be fully deployed by the end of 2026. The infrastructure will optimise AI performance by clustering NVIDIA GB200 and GB300 GPUs via Amazon EC2 UltraServers for low-latency, large-scale processing.
These clusters will support tasks such as training new models and serving inference for ChatGPT.
OpenAI CEO Sam Altman said the partnership would help scale frontier AI securely and reliably, describing it as a foundation for ‘bringing advanced AI to everyone.’ AWS CEO Matt Garman noted that AWS’s computing power and reliability make it uniquely positioned to support OpenAI’s growing workloads.
The move strengthens an already active collaboration between the two firms. Earlier this year, OpenAI’s models became available on Amazon Bedrock, enabling AWS clients such as Peloton, Thomson Reuters, and Comscore to adopt advanced AI tools.
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Mercedes-Benz reported faster decisions and better on-time delivery at Celosphere 2025. Using Celonis within MO360, it unifies production and logistics data, extending visibility across every order, part, and process.
Order-to-delivery operations use AI copilots to forecast timelines, optimise sequencing, and cut delays. After-sales teams surface bottlenecks in service parts logistics and speed customer responses. Quality management utilises anomaly detection to identify deviations early, preventing them from impacting production output.
Executives say complete data transparency enables teams to act faster and with greater precision across production and supply chains. The approach helps anticipate change and react to market shifts. Hundreds of active users are expanding adoption as data-driven practices scale across the company.
Celonis positions process intelligence as the backbone that makes enterprise AI valuable. Integrated process data and business context create a live operational twin. The goal is moving from visibility to action, unlocking value through targeted fixes and intelligent automation.
Conference sessions highlighted broader momentum for process intelligence and AI in industry. Leaders discussed governance, standards, and measurable outcomes from digital platforms. Mercedes-Benz framed its results as proof that structured data and AI can lift performance at a global scale.
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Nissan and Monolith have extended their strategic partnership for three years to apply AI across more vehicle programmes in Europe. The collaboration supports Nissan. RE: Nissan Plans to Compress Development Timelines and Improve Operational Efficiency. Early outcomes are guiding a wider rollout.
Engineers at Nissan Technical Centre Europe will utilise Monolith to predict test results based on decades of data and simulations. Reducing prototypes and conducting targeted, high-value experiments enables teams to focus more effectively on design decisions. Ensuring both accuracy and coverage remains essential.
A prior project on chassis bolt joints saw AI recommend optimal torque ranges and prioritise the following best tests for engineers. Compared with the non-AI process, physical testing fell by 17 percent in controlled comparisons. Similar approaches are being prepared for future models beyond LEAF.
Leaders say that a broader deployment could halve testing time across European programmes if comparable gains are achieved. Governance encompasses rigorous validation before changes are deployed to production. Operational benefits include faster iteration cycles and reduced test waste.
Monolith’s toolkit includes next-test recommendation and anomaly detection to flag outliers before rework. Nissan frames the push as an innovation with sustainability benefits, cutting material use while maintaining quality across a complex supply chain. Partners will share results as adoption scales.
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